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Representation of human preference using folksonomy and the idea called concept

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Abstract

This paper presents an application of the idea called concept for realizing more appropriate representation of human preferences. In the previous study, we proposed the new information recommendation method. Concretely, items for recommendation were selected using the idea of concept, which are impressions of users on items inferred using tagging data of a folksonomy. In the method, characteristics of items were represented by concepts, and it is expected that preferences of users can be represented by concepts as well. However, accuracy of concepts is influential in this approach. In this study, we investigated the validity of the obtained concepts using the previous proposed method, and proposed the improved derivation method of concepts. The effectiveness of the proposed method was verified trough comparison experiments with the previous method.

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References

  1. Yamaba H, Tanoue M, Takatsuka K et al (2013) On a serendipity-oriented recommender system based on folksonomy. Artif Life Robot 18:89–94

    Article  Google Scholar 

  2. Kamishima T (2008) Algorithms for recommender system (2) (in Japanese). JSAI 23:89–103

    Google Scholar 

  3. Schager JB, Frankowski D, Jon H et al (2007) Collaborative filtering recommender systems, the Adaptive Web, LNCS4321, pp 291–324

  4. Pazzani JM, Billsus D (2007) Content-based recommender systems, the Adaptive Web, LNCS4321, pp 325–541

  5. Szomszor M, Cattuto C, Alani H et al (2007) Folksonomy, the semantic web, and movie recommendation. Bridging Gap Between Semantic Web Web 2:71–84

    Google Scholar 

  6. Niwa S, Doi T, Honiden S (2006) Web page recommender system based on Folksonomy mining (in Japanese). IPSJ 47(5):1382–1392

    Google Scholar 

  7. Shepitsen A, Gemmell J, Mobasher B et al (2008) Personalized recommendation in social tagging systems using Hierarchical Clustering. In: Proceeding of the 2008 ACM conference on recommender systems, pp 259–266

  8. Krestel R, Frankhauser P (2009) Tag recommendation using probabilistic topic models. ECML PKDD Disc Chall 2009:131–141

    Google Scholar 

  9. Said A, Wetsker R, Umbrath W et al (2009) A hybrid PLSA approach for warmer cold start in folksonomy recommendation. Recommender System and the Social Web, pp 87–90

  10. Chan PK (1999) A non-invasive learning approach to building web user profiles, KDD.99 Work-shop on Web Usage Analysis and User Profiling

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Correspondence to Hisaaki Yamaba.

Additional information

This work was presented in part at the 19th International Symposium on Artificial Life and Robotics, Beppu, Oita, January 22–24, 2014.

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Yamaba, H., Tanoue, M., Takatsuka, K. et al. Representation of human preference using folksonomy and the idea called concept. Artif Life Robotics 19, 299–304 (2014). https://doi.org/10.1007/s10015-014-0170-0

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  • DOI: https://doi.org/10.1007/s10015-014-0170-0

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